library(tidyverse)
library(cowplot)
library(broom)
library(plotly)
# data
data1 <- data %>%
gather(Sample,Count,2:49)
# Separate samples by identifiers
data2 <- data1 %>%
separate(Sample, into=c("Sample_ID","Dilution_factor",
"Injection","Tech_rep", sep = "_")) %>%
select(-`_`)
# Standards
standards1 <- standards %>%
gather(Sample,Count,2:25)
standards2 <- standards1 %>%
separate(Sample, into=c("Sample_ID","When","Dilution_factor",
"Nano_day","Injection","Tech_Rep", sep = "_")) %>%
select(-`_`)
# Refactoring Columns for samples
data2$Sample_ID <- as.factor(data2$Sample_ID)
data2$Dilution_factor <- as.numeric(data2$Dilution_factor)
data2$Injection<- as.factor(data2$Injection)
data2$Tech_rep <- as.numeric(data2$Tech_rep)
data2
# Refactoring COlumns for key
key$Sample_ID <- as.factor(key$Sample_ID)
key$Time <- as.factor(key$Time)
key$Treatment <- as.factor(key$Treatment)
key$Volume <- as.numeric(key$Volume)
key$Treatment <- factor(key$Treatment,levels = c('DMSO','EGF','BPS','BPS_EGF'))
key
# Refactoring columns for standards
standards2$Sample_ID <- as.factor(standards2$Sample_ID)
standards2$When <- as.factor(standards2$When)
standards2$Dilution_factor <- as.numeric(standards2$Dilution_factor)
standards2$Injection <- as.factor(standards2$Injection)
standards2$Nano_day <- as.numeric(standards2$Nano_day)
standards2
standards2 <- standards2 %>%
mutate(True_Count=Dilution_factor*Count)
# Set the correct order of 'categorical factors'
standards2$Nano_day <- factor(standards2$Nano_day, levels=c('1','2'))
standards2$When <- factor(standards2$When, levels=c('before','after'))
standards2
standards3 <- standards2 %>%
group_by(particle_size,Sample_ID,When,Dilution_factor,Nano_day,Injection) %>%
summarise( tech_N = length(True_Count),
tech_mean = mean(True_Count),
tech_sd = sd(True_Count),
tech_se = tech_sd/sqrt(tech_N))
standards3
standards4 <- standards3 %>%
group_by(Nano_day,When,particle_size) %>%
summarise( inj_N = length(tech_mean),
inj_mean = mean(tech_mean),
inj_sd = sd(tech_mean),
inj_se = inj_sd/sqrt(inj_N))
standards4
std_plot <- standards4 %>%
ggplot(aes(x = particle_size, y = inj_mean, color=When))+
geom_line(size=2) + xlim(0,300)+ #line size, x-axis scale
geom_ribbon(aes(ymin=inj_mean-inj_se, ymax=inj_mean+inj_se),
alpha=0.4,fill = alpha('grey12', 0.2)) + #error bars
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Particle Size") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\n100nm Standards")+ #title
labs(color="Condition")+ #Label table title
facet_wrap(~ Nano_day)
std_plot
## Warning: Removed 1400 rows containing missing values (geom_path).
# ggsave("Standards_histogram_plot.png",
# height = 5, width = 7, dpi = 300, units= "in")
standards_df <- standards4 %>%
group_by(Nano_day,When) %>%
summarise(total=sum(inj_mean))
standards_df
standards_bar <- standards_df %>%
ggplot(aes(x=Nano_day,y=total,fill=When))+
geom_col(position="dodge")+
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Experimental Day") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\n100nm Standards")+ #title
labs(color="When") #Label table title
standards_bar
# ggsave("Standards_bar_plot.png",
# height = 5, width = 7, dpi = 300, units= "in")
Intra.assay_cv <- standards_df %>%
group_by(Nano_day) %>%
summarise(Intra_Day_N = length(total),
Intra_Day_mean = mean(total),
Intra_Day_sd = sd(total),
Intra_Day_se = Intra_Day_sd/sqrt(Intra_Day_N),
Intra_Day_cv = Intra_Day_sd/Intra_Day_mean )
Intra.assay_cv
# # Save as .csv
# write_csv(Intra.assay_cv,"Intra.assay_cv.csv")
Inter.assay_cv <- Intra.assay_cv %>%
summarise(Inter_Day_N = length(Intra_Day_mean),
Inter_Day_mean = mean(Intra_Day_mean),
Inter_Day_sd = sd(Intra_Day_mean),
Inter_Day_se = Inter_Day_sd/sqrt(Inter_Day_N),
Inter_Day_cv = Inter_Day_sd/Inter_Day_mean )
Inter.assay_cv
# # Save as .csv
# write_csv(Inter.assay_cv,"Inter.assay_cv.csv")
data2 <- data2 %>%
mutate(True_Count = Dilution_factor*Count)
data2
data3 <- data2 %>%
group_by(particle_size,Sample_ID,Dilution_factor,Injection) %>%
summarise( tech_N = length(True_Count),
tech_mean = mean(True_Count),
tech_sd = sd(True_Count),
tech_se = tech_sd/sqrt(tech_N))
data3
data4 <- data3 %>%
group_by(particle_size,Sample_ID,Dilution_factor) %>%
summarise( inj_N = length(tech_mean),
inj_mean = mean(tech_mean),
inj_sd = sd(tech_mean),
inj_se = inj_sd/sqrt(inj_N))
data4
# Average technical replicates and merge with key
merge <- left_join(key,data3, by= "Sample_ID")
merge
# Save as .csv
# write_csv(merge,"Technical_replicate_average.csv")
# Average injection replicates and merge with key
merge1 <- left_join(key,data4, by= "Sample_ID")
merge1
# #Save as .csv
# write_csv(merge1,"Injection_replicate_average.csv")
sample_plot <- merge %>%
ggplot(aes(x=particle_size, y=tech_mean,color=Injection ))+ #plot
geom_ribbon(aes(ymin=tech_mean-tech_se,
ymax=tech_mean+tech_se),
alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
geom_line(size=2.0, alpha = 0.8) + xlim(0,500)+ #line size, x-axis scale
scale_y_continuous(expand=c(0,0))+ #set bottom of graph
xlab("Particle Size") + # X axis label
ylab("\nMean Particle Concentration/ml\n") + # Y axis label
ggtitle("Nanosight Histogram of\nhCTBS treated with BPS")+ #title
labs(color="Injection")+ #Label table title
facet_grid(Time ~ Treatment)
# geom_vline(xintercept = 200)+
# annotate("text", x= 350, y = 1E8, label= "200nm")
sample_plot
# ggsave("Nanosight_Sample_Histogram.png", plot = sample_plot,
# height = 10, width = 14, dpi = 200, units= "in")
ggplotly(sample_plot)
merge2 <- merge1 %>%
group_by(Time, Treatment, Volume) %>%
summarise(particle_conc=sum(inj_mean))
merge2
merge3 <- merge2 %>%
mutate(particle_count = (Volume/1000)*particle_conc, # Create new column with number of particles
corrected_particle_conc = (particle_conc/1E9)) # Create new column with correct particle concentration
merge3
# Save as .csv
# write_csv(merge3,"Adjusted_particle_concentration.csv")
plot1 <- merge3 %>%
ggplot(aes(x = Treatment, y = corrected_particle_conc, fill = Treatment)) +
geom_bar(aes(text = paste("Particle Concentration:",
corrected_particle_conc)),
stat="identity", position = "dodge")+
xlab("\nTreatment\n") + # X axis label
ylab("\nMean Vessicle Concentration\n(10^9 particles/ ml)\n") + # Y axis label
ggtitle("Effect of BPS on Extracellular Vessicle\nRelease of hCTBs\n")+
scale_y_continuous(breaks = seq(0,14,2),
limits = c(0,14),
expand = c(0,0))+ # set bottom of graph
labs(color="Condition")+ # Label table title
facet_wrap(~Time)
## Warning: Ignoring unknown aesthetics: text
plot1
# ggsave("BPS_treated_hCTBs_48_96_facet_plot.png",
# height = 8, width = 10, dpi = 600, units= "in")
ggplotly(plot1)
plot2 <- merge3 %>%
group_by(Time) %>%
ggplot(aes(x = Treatment, y = corrected_particle_conc, fill = Time )) +
geom_bar(position = "dodge", stat = "identity")+
xlab("\nTreatment\n") + # X axis label
ylab("\nMean Vessicle Concentration\n(10^9 particles/ ml)\n") + # Y axis label
ggtitle("Effect of BPS on Extracellular Vessicle\nRelease of hCTBs\n")+
scale_y_continuous(breaks = seq(0,14,2),
limits = c(0,14),
expand = c(0,0))+ # set bottom of graph
labs(fill= "Time (hr)")
plot2
# ggsave("BPS_treated_hCTBs_48_96_plot.png",
# height = 5, width = 7, dpi = 600, units= "in")
ggplotly(plot2)
fit <- aov(corrected_particle_conc ~ Time * Treatment ,data=merge3)
tidy(fit)